What my 11-Kilometre Bicycle ride to School taught me about Learning AI

Hello Friends,

Hope you are all doing well and had a great summer vacation !!!

Most, if not all, of my posts are technical. However, this post is slightly different. We frequently use “simple” and “easy” as if they mean the same thing. I believe they are different. Understanding this subtle difference can change how we approach learning something new, such as AI, or developing any other technical or life skill. It can also help us see our progress more clearly.

We will try to understand this concept better in this post. But before that, let me take you back to my childhood and an 11-kilometre bicycle ride to school that taught me this lesson long before I started learning AI. By the way, it was 11 kilometres one way, so the total journey was 22 kilometres every day 🙂

A small lesson from Bicycle

I come from a small village in Bihar called Chausa, and my school was nearly 11 kilometres from home. I used to take the bus to reach there.

By the time I reached Class 9, learning to ride a bicycle had become a small mission for me. Whenever a guest visited us on a bicycle, I would borrow it for a ride.

I still remember the first time I thought I could finally ride on my own. A few seconds later, I was in a roadside trench 🙂 Fortunately, I was not seriously hurt, so I got up and tried again.

By Class 10, I was cycling those same 11 kilometres to school and 11 kilometres back every day. I was not only able to do it, I enjoyed it too. Sometimes, I would stop midway for a samosa, and even today, that memory brings back a lot of nostalgia.

Initially, riding a bicycle felt difficult. I had to think about balancing, pedalling, steering, watching the broken road ahead (that was the condition of the roads in my village), and applying the brakes at the same time. But with practice, all of it started coming naturally. None of these activities disappeared. However, I no longer had to think about each of them separately.

My bicycle had not changed. The basic task of cycling had not changed. My experience of performing it had changed.

Simple and Easy are not the same

This made me realise that we often treat two different ideas as if they mean the same thing. The words “simple” and “easy” are a good example.

Simple or complex describes the nature of a task. It tells us how many elements, dependencies, decisions, skills, or interactions are involved.

Easy or difficult describes our experience of performing that task. It depends on our knowledge, practice, confidence, context, and familiarity.

If we place these two dimensions together, we get four possible combinations:

Nature of Task ↓ / Experience →	Easy	Difficult
Simple	Q1 – Everyday task Brushing teeth Walking Drinking water	Q2 – Self Discipline Learning to ride a bicycle Meditation Waking up early
Complex	Q3 – Mastery ⭐ Experienced developer writing code Experienced architect designing solutions Experienced public speaker	Q4 – Growth Zone 🚀 Learning AI Investing Writing a book Starting a company

I understand that context matters and these examples are not permanent labels. So, driving car may feel easy on a familiar road and difficult in an unfamiliar city. However, the idea is to represent that simple does not automatically mean easy, and complex does not automatically mean difficult.

It is a journey, not just a framework

Whenever we decide to learn something valuable, whether it is AI, software architecture, investing, writing, leadership, or public speaking, we generally begin in the Q4 which is Complex + Difficult quadrant. That is where learning and growth begin so I call it Growth Zone.

Now, the nature of task normally doesn’t change. A complex task often remains complex. What can change is our experience of performing it. As we learn, practise, and repeat, we move left in the same row, from Q4 to Q3. I call Q3 the Mastery Zone.

Take software development as an example. An experienced developer still need to consider architecture, performance, security, integrations, and business needs. However, familiar coding, common debugging steps, development tools, and design patterns gradually become second nature. As a result, the same task feels easier to an experienced developer than it does to someone who is just beginning.

The key is Habit…

This is where practice becomes important. Repeated practice turns familiar actions into patterns. Over time, some of those patterns require less conscious effort and gradually become habits.

A habit does not reduce complexity, it makes the repeatable parts of execution more automatic. In AI terms, its like Agentic AI where routine execution can happen with less intervention, while judgement is still needed for exceptions and important decisions.

The journey looks something like this:

Repeated practice → Familiarity → Habit → More automatic execution → Easier execution

This does not mean that every part of a complex task becomes automatic. An experienced developer does not stop thinking, and a good leader cannot manage every person using a fixed script.

Habits automate the repeatable parts and make execution easier. Experience helps us recognise familiar situations. This leaves more attention for the parts that genuinely need judgement.

Moving from Q4 to Q3

As we discussed, most growth activities begin in Q4, and our objective should be to move towards the Mastery Zone, Q3. There are many detailed guides on how to build a habit, but in my view, a few practical actions can help:

• Start with something small and useful. Broad objectives such as “learn AI,” “learn investing,” or “become a writer” are difficult to practise. Select one activity or use case that can be completed and repeated.
• Create a regular rhythm. A small amount of regular practice is usually more useful than one long session followed by a gap of several weeks. Regularity helps us retain the context and makes it easier to begin again.
• Repeat before moving to the next thing. Exploring new tools and topics gives us exposure, but repetition creates familiarity. Repeat the same fundamental steps until they no longer require the same conscious effort.
• Review and reuse. Check the outcome, understand what failed, and improve the next attempt. Notes, templates, checklists, and reusable code also help us avoid starting from zero every time

How I am using this approach to learn AI

As an architect, I need to keep learning new technologies. Today, much of the technology buzz is around AI, but AI is also very broad. So, keeping “learn AI” as one large objective can quickly become overwhelming.

Therefore, I normally select a small use case related to my work, and then build it, test it, and improve it. While working on few of my latest projects, I repeated the same basic activities across different use cases. Gradually, the tools, common errors, and development process became more familiar.

This has not made AI simple. Data quality, security, integration, evaluation, governance, and business value still require judgement. However, I no longer start from zero every time.

I follow the same approach for any new technology: start with a relevant use case, practise regularly, and reuse what I learn. For an architect, the real habit is not learning one technology. It is becoming comfortable with learning new technologies again and again.

Finally, back to the bicycle

When I started learning to ride a bicycle, balancing, pedalling, steering, and braking competed for my attention. A year later, I could cycle 22 kilometres every school day without consciously managing every movement.

The bicycle did not become simpler. The road did not become shorter. I changed through practice.

Perhaps the same principle applies when we learn a new skill, write, invest, lead people, speak in public, or build complex technology solutions. We should simplify a process where it is genuinely possible. However, where complexity remains, we should focus on making the repeatable parts more familiar and easier to execute.

So, that was all in this post. This was my first attempt to write about an idea that is not directly technical, and I hope it helps you take away a few practical ideas for your own learning journey.

I will be back with some other technical or related stuff. Till then, keep practising 🙂